32 citations · 133 across the 15 of their papers we have counts for
8 papers · 1 filter
Empirical Bayes Shrinkage of Functional Effects, with Application to Analysis of Dynamic eQTLs
Ziang Zhang, Peter Carbonetto, Matthew Stephens
We introduce functional adaptive shrinkage (FASH), an empirical Bayes method for joint analysis of observation units in which each unit estimates an effect function at several valu…
Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto +3
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is…
Covariate-moderated Empirical Bayes Matrix Factorization
William R. P. Denault, Karl Tayeb, Peter Carbonetto +2
Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with "side…
Gradient-based optimization for variational empirical Bayes multiple regression
Saikat Banerjee, Peter Carbonetto, Matthew Stephens
Variational empirical Bayes (VEB) methods provide a practically attractive approach to fitting large, sparse, multiple regression models. These methods usually use coordinate ascen…
Improved methods for empirical Bayes multivariate multiple testing and effect size estimation
Yunqi Yang, Peter Carbonetto, David Gerard +1
Estimating the sharing of genetic effects across different conditions is important to many statistical analyses of genomic data. The patterns of sharing arising from these data are…
A flexible model for correlated count data, with application to multi-condition differential expression analyses of single-cell RNA sequencing data
Yusha Liu, Peter Carbonetto, Michihiro Takahama +4
Detecting differences in gene expression is an important part of single-cell RNA sequencing experiments, and many statistical methods have been developed for this aim. Most differe…
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
Youngseok Kim, Wei Wang, Peter Carbonetto +1
We introduce a new empirical Bayes approach for large-scale multiple linear regression. Our approach combines two key ideas: (i) the use of flexible "adaptive shrinkage" priors, wh…
Flexible signal denoising via flexible empirical Bayes shrinkage
Zhengrong Xing, Peter Carbonetto, Matthew Stephens
Signal denoising---also known as non-parametric regression---is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed d…